Gust Load Alleviation (GLA) is an important aspect of flight dynamics and control that reduces structural loadings and enhances ride quality. In conventional GLA systems, the structural response to aerodynamic excitation informs the control scheme. A phase lag, imposed by inertia, between the excitation and the measurement inherently limits the effectiveness of these systems. Hence, direct measurement of the aerodynamic loading can eliminate this lag, providing valuable information for effective GLA system design. Distributed arrays of Artificial Hair Sensors (AHS) are ideal for surface flow measurements that can be used to predict other necessary parameters such as aerodynamic forces, moments, and turbulence. In previous work, the spatially distributed surface flow velocities obtained from an array of artificial hair sensors using a Single-State (or feedforward) Neural Network were found to be effective in estimating the steady aerodynamic parameters such as air speed, angle of attack, lift and moment coefficient. This paper extends the investigation of the same configuration to unsteady force and moment estimation, which is important for active GLA control design. Implementing a Recurrent Neural Network that includes previous-timestep sensor information, the hair sensor array is shown to be capable of capturing gust disturbances with a wide range of periods, reducing predictive error in lift and moment by 68% and 52% respectively. The L2 norms of the first layer of the weight matrices were compared showing a 23% emphasis on prior versus current information. The Recurrent architecture also improves robustness, exhibiting only a 30% increase in predictive error when undertrained as compared to a 170% increase by the Single-State NN. This diverse, localized information can thus be directly implemented into a control scheme that alleviates the gusts without waiting for a structural response or requiring user-intensive sensor calibration.

Origami devices have the ability to spatially reconfigure between 2D and 3D states through folding motions. The precise mapping of origami presents a novel method to spatially tune radio frequency (RF) devices, including adaptive antennas, sensors, reflectors, and frequency selective surfaces (FSSs). While conventional RF FSSs are designed based upon a planar distribution of conductive elements, this leaves the large design space of the out of plane dimension underutilized. We investigated this design regime through the computational study of four FSS origami tessellations with conductive dipoles. The dipole patterns showed increased resonance shift with decreased separation distances, with the separation in the direction orthogonal to the dipole orientations having a more significant effect. The coupling mechanisms between dipole neighbours were evaluated by comparing surface charge densities, which revealed the gain and loss of coupling as the dipoles moved in and out of alignment via folding. Collectively, these results provide a basis of origami FSS designs for experimental study and motivates the development of computational tools to systematically predict optimal fold patterns for targeted frequency response and directionality.

An active area of research in adaptive structures focuses on the use of continuous wing shape changing methods as a
means of replacing conventional discrete control surfaces and increasing aerodynamic efficiency. Although many shape-changing
methods have been used since the beginning of heavier-than-air flight, the concept of performing camber
actuation on a fully-deformable airfoil has not been widely applied. A fundamental problem of applying this concept to
real-world scenarios is the fact that camber actuation is a continuous, time-dependent process. Therefore, if camber
actuation is to be used in a closed-loop feedback system, one must be able to determine the instantaneous airfoil shape as
well as the aerodynamic loads at all times. One approach is to utilize a new type of artificial hair sensors developed at
the Air Force Research Laboratory to determine the flow conditions surrounding deformable airfoils. In this work, the
hair sensor measurement data will be simulated by using the flow solver XFoil, with the assumption that perfect data
with no noise can be collected from the hair sensor measurements. Such measurements will then be used in an artificial
neural network based process to approximate the instantaneous airfoil camber shape, lift coefficient, and moment
coefficient at a given angle of attack. Various aerodynamic and geometrical properties approximated from the artificial
hair sensor and artificial neural network system will be compared with the results of XFoil in order to validate the
approximation approach.

Artificial hair sensors have been developed in the Air Force Research Laboratory for use in prediction of local flow
around airfoils and subsequent use in gust rejection applications. The on-going sensor development is based on a micro-sized
unmanned vehicle, resulting in a sensor design that is sensitive in that aircraft’s nominal flight condition (speed).
However, the active, or operating, region of the artificial hair sensor concept is highly dependent on the geometry and
properties of the hair, capillary, and carbon nanotubes that make up the sensor design. This paper aims at expanding the
flow measurement concept using artificial hair sensors to UAVs with different dimensions by properly sizing the
parameters of the sensors, according to the nominal flight conditions of the UAVs. In this work, the hair, made of glass
fiber, will be modeled as a cantilever beam with an elastic foundation, subject to external distributed aerodynamic drag.
Hair length, diameter, capillary depth, and carbon nanotube length will be scaled by keeping the maximum strain of the
carbon nanotubes constant for different sensors under different working conditions. Numerical studies will demonstrate
the feasibility of the scaling methodology by designing artificial hair sensors for UAVs with different dimensions and
flight conditions, starting from a baseline sensor design.

Origami structures morph between 2D and 3D conformations along predetermined fold lines that efficiently program the form, function and mobility of the structure. The transfer of origami concepts to engineering design shows potential for many applications including solar array packaging, tunable antennae, and deployable sensing platforms. However, the enormity of the design space and the complex relationship between origami-based geometries and engineering metrics places a severe limitation on design strategies based on intuition. This motivates the development of design tools based on optimization to identify optimal fold patterns for geometric and functional objectives. The present work proposes a topology optimization method using mechanical analysis to distribute fold line properties within a reference crease pattern to achieve a target actuation. By increasing the fold stiffness, unnecessary folds are effectively removed from the design solution, which allows fundamental topologies for actuation to be identified. A series of increasingly refined reference grids were analyzed and several actuating mechanisms were predicted. The fold stiffness optimization was then followed by a node position optimization, which determined that only two of the predicted topologies were fundamental and the solutions from higher density grids were variants or networks of these building blocks. This two-step optimization approach provides a valuable check of the grid dependency of the design and offers an important step toward systematic incorporation of origami design concepts into new, novel and reconfigurable engineering devices.

Artificial hair sensors (AHS) have been recently developed in Air Force Research Laboratory (AFRL) using carbon nanotube (CNT). The deformation of CNT in air flow causes voltage and current changes in the circuit, which can be used to quantify the dynamic pressure and aerodynamic load along the wing surface. AFRL has done a lot of essential work in design, manufacturing, and measurement of AHSs. The work in this paper is to bridge the current AFRL’s work on AHSs and their feasible applications in flight dynamics and control (e.g., the gust alleviation) of highly flexible aircraft. A highly flexible vehicle is modeled using a strain-based geometrically nonlinear beam formulation, coupled with finite-state inflow aerodynamics. A feedback control algorithm for the rejection of gust perturbations will be developed. A simplified Linear Quadratic Regulator (LQR) controller will be implemented based on the state-space representation of the linearized system. All AHS measurements will be used as the control input, i.e., wing sectional aerodynamic loads will be defined as the control output for designing the feedback gain. Once the controller is designed, closed-loop aeroelastic simulations will be performed to evaluate the performance of different controllers with the force feedback and be compared to traditional controller designs with the state feedback. From the study, the feasibility of AHSs in flight control will be assessed. The whole study will facilitate in building a fly-by-feel simulation environment for autonomous vehicles.

Crickets, locusts, bats, and many other animals detect changes in their environment with distributed arrays of flow-sensitive hairs. Here we discuss the fabrication and characterization of a relatively new class of pore-based, artificial hair sensors that take advantage of the mechanical properties of structural microfibers and the electromechanical properties of self-aligned carbon nanotube arrays to rapidly transduce changes in low speed air flow. The radially aligned nanotubes are able to be synthesized along the length of the fibers inside the high aspect ratio cavity between the fiber surface and the wall of a microcapillary pore. The growth self-positions the fibers within the capillary and forms a conductive path between detection electrodes. As the hair is deflected, nanotubes are compressed to produce a typical resistance change of 1-5% per m/s of air speed which we believe are the highest sensitivities reported for air velocities less than 10 m/s. The quasi-static response of the sensors to point loads is compared to that from the distributed loads of air flow. A plane wave tube is used to measure their dynamic response when perturbed at acoustic frequencies. Correlation of the nanotube height profile inside the capillary to a diffusion transport model suggests that the nanotube arrays can be controllably tapered along the fiber. Like their biological counterparts, many applications can be envisioned for artificial hair sensors by tailoring their individual response and incorporating them into arrays for detecting spatio-temporal flow patterns over rigid surfaces such as aircraft.

This paper represents a "work-in-progress" status report on shape determination and sensing methods for deforming structures utilizing embedded sensors. This work is part of a larger effort in morphing aircraft structures at the Air Force Research Laboratory. The two critical issues involved in the present work are the determination of the number and placement of embedded sensors, and algorithms for transforming the sensor data into displacements to define the shape of the structure. These issues are addressed in the context of a laboratory experiment, which demonstrate many of the challenges
inherent in the problem.

This paper presents an exploration of the Simulation Based Research and Development through an in-house technology assessment of a Sensorcraft concept. The goals of SBR&D are: to reduce the time and cost for developing and maturing promising technology, to integrate the technologist and the warfighter into the Science and Technology (S&T) acquisition process, and to provide analytical input into the Air Force S&T planning process. SBR&D combines a variety of critical research and technology-development capabilities, including engineering-level modeling, design, and analysis tools, mission- and campaign-level simulations, cost analysis tools, and database tools in a networked, distributed environment. Early SBR&D capabilities combine high fidelity manned and unmanned vehicle simulations to create a common synthetic battlespace for technology assessment in a mission environment. The simulation environment is being combined with engineering models, design tools, and an intelligent database to allow differing degrees of fidelity to be used at different times and in different parts of a simulation analysis. The study presented here represents an attempt to show the SBR&D process in action and to identify deficiencies in the process. Once established, the SBR&D process will provide the capability for researchers to evaluate the impact of different technologies in a warfighting environment, providing a link between AFRL technologies and warfighter mission needs.

A theory for localized vibration control that is based on a partitioned Linear Quadratic Regulator (LQR) synthesis is presented. The present localized control consists of two components: a localized LQR controller that minimizes the control effort to attenuate the disturbances directly applied to each partitioned substructural system, and a controller that mitigates the interface transmission forces. The present theory is applicable both for quasistatic structural shape control and for the attenuation of structural vibrations. The localized controllers can be implemented in terms of strain actuation, proof-mass actuators, and strain rate-type active dampers. The basic features of the present theory are illustrated via numerical experiments as applied to the control of vibrations of a beam.

This paper reviews a method of localized structural health monitoring based on relative changes in localized flexibility properties. The localized flexibility matrices are obtained either by applying a decomposition procedure to an experimentally determined global flexibility matrix or by processing the output signals of a vibration test in a substructure-by-substructure manner. The theory is based on the partitioning of the energy functional of a discrete dynamic system, for which Lagrange multipliers are utilized to enforce compatibility constraints between neighboring substructural regions. The resultant dynamics are then stated in terms of generalized variables that are unique to each substructure and the Lagrange multipliers that can be considered as interface forces which transfer energy between substructures. This theory is demonstrated with an experimental damage detection test of a bridge column model.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Keywords/Phrases

Keywords

in

Remove

in

Remove

in

Remove

+ Add another field

Search In:

Proceedings

Volume

Journals +

Volume

Issue

Page

Journal of Applied Remote SensingJournal of Astronomical Telescopes Instruments and SystemsJournal of Biomedical OpticsJournal of Electronic ImagingJournal of Medical ImagingJournal of Micro/Nanolithography, MEMS, and MOEMSJournal of NanophotonicsJournal of Photonics for EnergyNeurophotonicsOptical EngineeringSPIE Reviews